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Improving Physical Security by Improving Indoor Security Surveillance

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Introduction
As the topic name suggests this thesis paper is a great stride forward for improving physical security by improving indoor security surveillance. Physical security is a multilayered phenomenon comprising of:
• Environmental Design that deters threats
• Access control which restricts access to unauthorized entities
• Intrusion Detection Systems(IDS)
• Identification and incident verification(video surveillance)
Objective
The objective of this research was to improve indoor security by expanding the intrusion detection to support the functionality of Automatic Intruder Identification and Tracking System (AIITS) through [1]:
• Describing an architecture for the AIITS, built upon BIM, UWB RTLS and PTZ camera,
• Defining and analyzing the requirements of the proposed architecture,
• Elaborating on the proposed methodology for technology integration, data fusion, intruder tracking, and post-event XQuery processing,
• Implementing a prototype that can validate the proposed approach using a case study.
Here the framework comprises of BIM(Building Information Modelling), a geospatial data source that helps in getting a reliable decision based on optimum sensor networks, finding the intruder location and routing the shortest path of the located intruder to the security officers.

Using BIM in Security system
• Deployment: Sensors Simulation coverage and finding out the respective blind spots in each room to achieve the optimum design of deployment.
Access control: Storing the rooms’ usage constraints:
• Check whether the room is public or private
• Access Control Lists (ACLs) that contains the ID and authorization schedule of the authorized persons.
• Localization validation: Validate UWB’s location data by mapping them in...

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...his leads to a false positive which shows wrong number of intruders while data fusion.
2) Error 2: A tag out of FoV was positioned inside the FoV which leads to a false negative that doesn’t identify the correct intruder.
3) Error 3: The tags are positioned erroneously resulting in error prone associations which again lead to wrong intruder identification.
Conclusion
It was a great attempt to enhance physical security. Data fusion accuracy can be affected by two error sources basically by inaccurate UWB positioning and False positive/negative CV-based detection. The quality of data fusion also depends on accurate synchronisation and alignment of input datasets. However this study also shows a need to improve auto tracking and XQuery processing functions.

REFERENCES
[1] Mahsa Rafie, IMPROVING INDOOR SECURITY SURVEILLANCE
BY FUSING DATA FROM BIM, UWB AND VIDEO
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